Clearly presented and transparently reported statistical code is a sine qua non for reproducible research. More than a decade ago, Annals began asking authors to report the availability of code that supported their statistical methods (1). Before that policy adoption, our statistical editors routinely requested and reviewed code underlying analyses of papers that we eventually published. Although we never formally graded submitted code, our experiences are similar to those reported by Assel and Vickers (2).

We have found that authors increasingly apply complex statistical methods to account for such factors as correlated and repeated measures, missing data, incomplete adherence, and confounders on the causal pathway from exposure to outcomes. Reporting only that “SAS version 9.2 (SAS Institute) was used for all analyses” offers neither a hint of actual methods nor the model specification or structure. Naming a procedure or library within a statistical package usually explains little more. Authors have responded to our requests with computer code that frustrates interpretation except by the author–programmer, as if clear statistical code were not needed to understand or reproduce the science. We beg to differ. The goals of reproducible research in the biostatistics literature (3) apply equally to supporting statistical code in medical journals (4).